摘要
Under variable loading,fatigue life prediction is very important for the selection,design,and safety assessments of these components.In this study,based on the Miner rule,an improved damage accumulation rule was proposed to consider the strengthening and damaging of low amplitude loads.The complexity of fatigue phenomenon results in predicting fatigue life difficulty.Since grey models(GMs)only require a limited amount of data to estimate the behavior of unknown systems,they are used in this paper to account for the uncertainties resulting from various sources when fatigue life of component is predicted.An improved unequal interval GM(IUGM(1,1))has been developed and applied successfully to estimation of fatigue life.An example is used to illustrate how the method works.The results show that the proposed model not only overcomes the limitations of the traditional grey forecasting model of linear change series,but also increases the scope of GM in the fatigue life prediction of mechanical components,and its accuracy is higher than that of the traditional model.Moreover,the results indicate that the IUGM(1,1)is capable of predicting component fatigue life better than the traditional Miner rule,and yields a high prediction precision.
Under variable loading,fatigue life prediction is very important for the selection,design,and safety assessments of these components.In this study,based on the Miner rule,an improved damage accumulation rule was proposed to consider the strengthening and damaging of low amplitude loads.The complexity of fatigue phenomenon results in predicting fatigue life difficulty.Since grey models（GMs）only require a limited amount of data to estimate the behavior of unknown systems,they are used in this paper to account for the uncertainties resulting from various sources when fatigue life of component is predicted.An improved unequal interval GM（IUGM（1,1））has been developed and applied successfully to estimation of fatigue life.An example is used to illustrate how the method works.The results show that the proposed model not only overcomes the limitations of the traditional grey forecasting model of linear change series,but also increases the scope of GM in the fatigue life prediction of mechanical components,and its accuracy is higher than that of the traditional model.Moreover,the results indicate that the IUGM（1,1）is capable of predicting component fatigue life better than the traditional Miner rule,and yields a high prediction precision.
基金
Joint Funds of the National Natural Soience Foundation of China(NSAF)(No.U1330130)
